Healthcare

Objective

Scenario

Tools & Activities:

Outcome

To understand and implement Retrieval-Augmented Generation (RAG) as a key architectural pattern for LLMs to deliver evidence-based, trustworthy Q&A from vast medical literature.

Use Case

Building and querying an AI system that leverages a proprietary medical knowledge base to provide referenced and highly accurate answers to clinical and research inquiries.

Core Challenges

Information Overload: Healthcare SMEs struggle to manually review thousands of new papers to stay current.

Slow Decision Support: Traditional search methods are slow and do not provide concise, synthesized answers with source evidence.

Hallucination Risk: Typical / standard LLMs could respond with inaccurate or fabricated information.

Tools & Activities:

The course explores 

• • How to setup a RAG architecture to explore a medical paper and ground the respond within the information available in a given input (pdf research paper)
  • Prompt engineering.
  • Interacting with an LLM through the chat interface
  • Pinecone vector database configuration, chunking and storage, search and retrieval
  • n8n workflow automation

Outcome

Participants will gain the skills to deploy a trustworthy, evidence-based AI system that ensures high factual accuracy and provides instant, referenced answers to complex clinical or research questions.